The SALICON challenge [1]}, by offering the first large scale dataset for saliency, facilitated the development of deep saliency models. Several such models learn a mapping from deep feature space to the saliency space, where a pre-trained object recognition network acts as the feature encoder. The encoder is then fine-tuned for the saliency task. For example, DeepNet [2]} learns saliency using 8 convolutional layers, where only the first 3 layers are initialized from a pre-trained image classification model.
PDP [3]} treats the saliency map as a small scale probability map, and investigates different loss functions for gaze prediction. They also suggested the use of Bhattacharyya distance when the gaze map is treated as a small scale probability map.
The SALICON [4]} model uses multi-resolution inputs, and combines feature representations in the deep layers for saliency prediction.
Deepfix [5]} combines deep architectures of VGG, GoogleNet [6]}, and Dilated convolutions [7]} in a network and adds a central bias, to achieve a higher performance than previous models. SalGAN [8]} uses an encoder-decoder architecture and proposes the binary cross entropy (BCE) loss function to perform pixel-wise (rather than image-wise) saliency estimation. After pre-training the encoder-decoder, it uses a Generative Adversarial Network (GAN) [9]} to boost performance.
DVA [10]} uses multiple layer's representations, builds a decoder for each layer, and fuses them at the final stage for pixel-wise gaze prediction.
SAM [11]} uses an attention module and a LSTM [12]} network to attend to different salient regions in the image.
DeepGaze 2 [13]} uses the features at different layers of a pre-trained deep model and combines them with the prior knowledge (center-bias).
DSCLRCN [14]} uses multiple inputs by adding a contextual information stream, and concatenates the original representation and the contextual representation into a LSTM network for the final prediction.
